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Review of data security within energy blockchain:A comprehensive analysis of storage,management,and utilization

High-Confidence Computing 2024年第3期4卷 82-102页

作者： Yunhua He Zhihao Zhou Yan Pan Fahui Chong Bin Wu Ke Xiao Hong Li School of Information Science and Technology North China University of TechnologyBeijing 100144China China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China Key Laboratory in Blockchain and Data Security by Ministry of Industry and Information Technology Beijing 100040China Institute of Information Engineering Chinese Academy of Sciences Beijing 100195China

Energy systems are currently undergoing a transformation towards new paradigms characterized by decarbonization,decentralization,democratization,and *** this evolving context,energy blockchain,aiming to enhance efficiency,transparency,and security,emerges as an integrated technological solution designed to address the diverse challenges in this *** security is essential for the reliable and efficient functioning of energy *** pressing need to address challenges related to secure data storage,effective data management,and efficient data utilization is increasingly *** paper offers a comprehensive survey of academic discourse on energy blockchain data security over the past five years,adopting an all-encompassing perspective that spans data storage,management,and *** work systematically evaluates and contrasts the strengths and weaknesses of various research ***,this paper proposes an integrated hierarchical on-chain and off-chain security energy blockchain architecture,specifically designed to meet the complex security requirements of multi-blockchain business ***,this paper identifies key directions for future research,particularly in advancing the integration of storage,management,and utilization of energy blockchain data security.

关键词： Energy blockchain Data security Storage Management Utilization

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation science and Engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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A polarization sensitive interferometer:Delta interferometer

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Chinese Physics B 2024年第3期33卷 369-377页

作者：卫超奇刘建彬董翼飞孙雨农周宇郑淮斌刘严严闫秀生李福利徐卓 Key Laboratory of Multifunctional Materials and Structures Ministry of Education & International Center for Dielectric ResearchSchool of Electronic Science and EngineeringXi'an Jiaotong UniversityXi'an 710049China MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter Department of Applied PhysicsXi'an Jiaotong UniversityXi'an 710049China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin 300308China

A new type of polarization sensitive interferometer is proposed,named the Delta interferometer,inspired by its geometry resembling the Greek letter *** main difference between the Delta interferometer and other existing interferometers,such as Michelson,Mach-Zehnder and Young's double-slit interferometers,is that the two interfering paths are asymmetrical in the Delta *** visibility of the first-order interference pattern observed in the Delta interferometer is dependent on the polarization of the incidental *** coherence theory is employed to interpret this phenomenon and single-mode continuous-wave laser light is employed to verify the theoretical *** theoretical and experimental results are *** Delta interferometer is a perfect tool to study the reflection of electromagnetic fields in different polarizations and may find applications in polarization-sensitive scenarios.

关键词： phase sensitive interferometer Fresnel formula first-order interference

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A Novel Orthogonal LoRa Multiple Access Algorithm for Satellite Internet of Things

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China communications 2022年第3期19卷 279-289页

作者： Chengwen Zhang Liankai Wang Libin Jiao Shipeng Wang Jun Shi Jia Yue School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150080China Science and Technology on Communication Networks Laboratory Shijiazhuang 050081China Key Laboratory of Police Wireless Digital Communication Ministry of Public SecurityHarbin 150001China

In recent years,LoRa has been extensively researched in the satellite Internet of Things(IoT).However,the multiple access technology of LoRa is still one of the bottlenecks of satellite *** improve the multiple access performance of LoRa satellite IoT,based on the orthogonality of LoRa symbols in the fractional domain,this paper proposes a low complexity Orthogonal LoRa Multiple Access(OLMA)algorithm for multiple LoRa users occupying the same frequency *** algorithm introduces the address code to divide the fractional bandwidth into multiple parts,and the OLMA users with different address codes occupy different parts to transmit the information code,thus avoiding mutual interference caused by collisions in the same frequency *** multiple access capability of OLMA can be flexibly configured only by simply adjusting the length of the address code according to the actual application requirements of data *** analysis and simulation results show that the OLMA algorithm can greatly improve the multiple access capability and the total transmission bit rate of LoRa IoT without changing the existing LoRa modulation parameters and process.

关键词： LoRa satellite Internet of Things multiple access fractional domain linear frequency modulation

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MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural Network Training Based on Functional Encryption

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Computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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Radio Signal Modulation Pattern Recognition Based on Time-Frequency Adaptive Decomposition and Hybrid Neural Network

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IEEE Internet of Things Journal 2025年

作者： Dai, Shaogang Xu, Jianwei Zhou, Huaji Hangzhou Dianzi University School of communication engineering Hangzhou310018 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314000 China

Automatic modulation classification of electromagnetic signals has an important role in the field of signal processing. The current methods for identifying the modulation pattern of radio signals mainly used end-to-end neural network models, which suffered from the problems such as network redundancy and non-interpretability. In order to solve the above problems, this paper proposes a radio signal modulation pattern recognition method based on time-frequency adaptive decomposition and hybrid neural network. The discrete wavelet decomposition is used to decompose the original radio signals into adaptive high and low frequencies. Then feed the resulting high and low frequency portions into a multi-channel hybrid neural network (MHNN) for training to obtain the classification results. In the discrete wavelet decomposition process, the number of decomposition layers and the decomposition threshold are determined adaptively according to the difference of SNRs. In the design of multi-path hybrid neural network structure, the signal characteristics of high and low frequency parts are considered comprehensively. The network channels with different convolutional kernel scales and different layers are constructed to reduce the number of model parameters and training time while ensuring the classification accuracy. Using RadioML2016.10a dataset for experiments, the recognition accuracy of MHNN is prior to the end-to-end deep learning methods, which can achieve the accuracy of 92.30% and the F1 of 91.84% when SNR=2dB. It shows that the proposed radio signal modulation pattern recognition method based on the time-frequency adaptive decomposition and hybrid neural network can balance the accuracy and lightweight. Moreover, it has certain interpretability for the original input data, distinguishing the main body and details of the signal by dividing the signal into high and low frequencies, which provides a new idea for the interpretability of deep convolutional neural networks. ©

关键词： Signal modulation

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Mainlobe DRFM Jamming Suppression via Blind Source Separation Using Tensor Decomposition 8

Mainlobe DRFM Jamming Suppression via Blind Source Separatio...

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Dong, Luxin Wang, Bing Pan, Bunian Kong, Yukai Yu, Xianxiang Cui, Guolong University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Science and Technology on Electronic Information Control Laboratory Chengdu610036 China

ISBN: (纸本)9798350397932

This paper deals with the suppression problem of mainlobe digital radio frequency memory (DRFM) jamming especially for intra-pulse slice forwarding jamming for phased array radar. A novel mainlobe DRFM jamming suppression method resorting to blind source separation based on tensor decomposition (TD-BSS) is proposed to separate the target echo signal and jamming signal. Specifically, the received signals are reconstructed into higher-order tensor form. Further, the separating matrix optimization problem relying on tensor Canonical Polyadic (CP) decomposition principle is formulated and then solved by an enhanced line search alternating least square (ELS-ALS) algorithm. Finally, the performance of the proposed method is demonstrated through numerical simulations, showing its superior to suppress mainlobe DRFM jamming over the exiting method in terms of interrupted sampling and cyclic repeater jamming (ISCRJ) and chopping and interval (C&I) jamming. © 2023 IEEE.

关键词： Blind source separation

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Underwater arbitrary ranging based on chirped pulse interferometry with a mode-locked laser

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Journal of Lightwave technology 2025年第11期43卷 5235-5243页

作者： Shi, Haonan Zhao, Haihan Xu, Xinyang Zhu, Zhiwei Zhai, Jingsheng Li, Xiaobo Tianjin University School of Marine Science and Technology Tianjin300072 China Research Institute of China Electronics Technology Group Corporation Science and Technology on Electro-Optical Information Security Control Laboratory No. 53 Tianjin300308 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong999077 Hong Kong Nanjing University of Aeronautics and Astronautics Key Laboratory of Space Photoelectric Detection and Perception Ministry of Industry and Information Technology Nanjing211106 China

Underwater distance measurement and length sensing, as fundamental functions of geoscience, are integrated into underwater robots and vessels in various forms of sensor payloads and play a crucial role in a wide range of remote sensing applications. Advanced laser-based detection systems and algorithms are essential for underwater tasks, such as detecting damage in underwater pipelines and conducting detailed structural health assessments. In this paper, we experimentally demonstrated an arbitrary underwater ranging system by the chirped pulse interferometry with a mode-locked laser. Water's inherent characteristic introduces dispersion, enabling unique distance discrimination. Adjusting the repetition frequency allows us to acquire the interference spectrum. A long fiber delay line, stabilized by the servo-controlled system, eliminates the ranging dead zone. The widest fringe of the interference spectrum, corresponding to an instantaneous repetition frequency, indicates the arbitrary measured distance. Leveraging a charge-coupled device detection module, we achieved an update rate of 13.5 Hz for the interference spectrum. Experimental results show a precise distance difference within 59.8 μm at the 5 m range underwater without the dead zone. © 2025 IEEE. All rights reserved.

关键词： Laser mode locking

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Classification of Deforestation Factors in 6G Satellite Forest Images 5th

Classification of Deforestation Factors in 6G Satellite Fore...

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5th EAI International Conference on 6G for Future Wireless Networks, 6GN 2022

作者： Li, Yuhai Sun, Yuxin Meng, Xianglong Xi, Liang Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300300 China School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9783031360107

The terrestrial satellite network will play an essential role in 6G. Through the satellite system, people can obtain a lot of ground image information to process tasks and feedback. Forest resource is an essential resource. Determining the causes of deforestation is crucial for the development and implementation of forest protection plans. In this article, we propose a novel deep neural network model for distinguishing drivers of deforestation events from satellite forest images. To solve the problems of image rotation caused by satellite angle rotation and image blurring caused by extreme weather occlusion during satellite image acquisition, we add data enhancement and a self-supervised rotation loss to the model to improve the robustness and adaptability. We use deforestation maps generated from Landsat 8 satellite imagery as a dataset and demonstrate that our approach achieves better results than the baselines. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Deforestation

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